Background: The classification of active compounds based on their function using machine
learning is essential for predicting the function of new active compounds quickly. These classification
results are beneficial to accelerate the work of laboratory assistants in identifying the
function of active compounds. In this study, an active compound is represented by the Simplified
Molecular-Input Line-Entry System (SMILES) code.
Objective: This paper proposes a modified acceleration coefficient to improve the PSO-ELM performance
for predicting the function of the SMILES code.
Methods: The research uses a machine-learning algorithm that is a combination of the Particle
Swarm Optimization and Extreme Learning Machine (PSO-ELM). ELM is used to classify the
SMILES code, while PSO is used to optimize ELM parameters, i.e., weight, bias, and the number
of hidden neurons. The important parameters that significantly influence the PSO performance are
acceleration coefficients. The acceleration coefficients, that are modified Sigmoid-Based Acceleration
Coefficient (SBAC), are introduced and compared with seven other acceleration coefficients.
Results: The experimental results show that the sensitivity, specificity, accuracy, and Area Under
the Curve (AUC) of the proposed acceleration coefficients outperform all other acceleration coefficients.
The increased accuracy of the proposed can reach up to 2.64%, 5.84%, 7.93%, 8.44%, and
16.29% for Support Vector Machine (SVM), decision tree, AdaBoost, MLP Classifier, and Gaussian
Naïve Bayes algorithms, respectively.
Conclusion: The acceleration coefficients affect the prediction accuracy of the SMILES code classification.
The proposed acceleration coefficients improve the performance of the PSO-ELM for
predicting the function of the SMILES code.